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钢丝绳断丝检测仪采集到的信号有高频低通等噪声信号的干扰,经过小波分解、重构后滤出干扰信号,采用BP神经网络算法设计钢丝绳检测信号网络系统。实验结果表明,采用BP神经网络算法可以较好地解决钢丝绳断丝信号的定量分析,最后给出了监测信号频谱上断丝信号的定量识别方法。经反复实验,检测结果与实际情况基本一致,证明该检测方法能较准确地对钢丝绳断丝做出定量识别。
The signals collected by the wire rope broken detector have the interference of the high-frequency low-pass and other noise signals. After the wavelet decomposition and reconstruction, the interference signals are filtered out. The BP neural network algorithm is used to design the wire rope detection signal network system. The experimental results show that the BP neural network algorithm can solve the quantitative analysis of the broken wire signal of the wire rope, and finally give the quantitative identification method of the broken wire signal in the monitoring signal spectrum. After repeated experiments, the test results and the actual situation is basically the same, proves that the test method can more accurately identify the wire rope broken wire quantitatively.